Process

Loading Packages

library(tidyverse)

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## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
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library(lubridate) 
library(dplyr)
library(ggplot2)
library(tidyr)
library(janitor)

## 
## Attaching package: 'janitor'
## 
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

library(skimr)
library(plotly)

## 
## Attaching package: 'plotly'
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## 
##     last_plot
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##     layout

Load Dataset

daily_activity <- read.csv("Capstone Project/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv")
sleep_day <- read.csv("Capstone Project/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv")
hourly_steps <- read.csv("Capstone Project/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv")

Preview Loaded Dataset

head(daily_activity)

##           Id ActivityDate TotalSteps TotalDistance TrackerDistance
## 1 1503960366    4/12/2016      13162          8.50            8.50
## 2 1503960366    4/13/2016      10735          6.97            6.97
## 3 1503960366    4/14/2016      10460          6.74            6.74
## 4 1503960366    4/15/2016       9762          6.28            6.28
## 5 1503960366    4/16/2016      12669          8.16            8.16
## 6 1503960366    4/17/2016       9705          6.48            6.48
##   LoggedActivitiesDistance VeryActiveDistance ModeratelyActiveDistance
## 1                        0               1.88                     0.55
## 2                        0               1.57                     0.69
## 3                        0               2.44                     0.40
## 4                        0               2.14                     1.26
## 5                        0               2.71                     0.41
## 6                        0               3.19                     0.78
##   LightActiveDistance SedentaryActiveDistance VeryActiveMinutes
## 1                6.06                       0                25
## 2                4.71                       0                21
## 3                3.91                       0                30
## 4                2.83                       0                29
## 5                5.04                       0                36
## 6                2.51                       0                38
##   FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes Calories
## 1                  13                  328              728     1985
## 2                  19                  217              776     1797
## 3                  11                  181             1218     1776
## 4                  34                  209              726     1745
## 5                  10                  221              773     1863
## 6                  20                  164              539     1728

colnames(daily_activity)

##  [1] "Id"                       "ActivityDate"            
##  [3] "TotalSteps"               "TotalDistance"           
##  [5] "TrackerDistance"          "LoggedActivitiesDistance"
##  [7] "VeryActiveDistance"       "ModeratelyActiveDistance"
##  [9] "LightActiveDistance"      "SedentaryActiveDistance" 
## [11] "VeryActiveMinutes"        "FairlyActiveMinutes"     
## [13] "LightlyActiveMinutes"     "SedentaryMinutes"        
## [15] "Calories"

str(daily_activity)

## 'data.frame':    940 obs. of  15 variables:
##  $ Id                      : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityDate            : chr  "4/12/2016" "4/13/2016" "4/14/2016" "4/15/2016" ...
##  $ TotalSteps              : int  13162 10735 10460 9762 12669 9705 13019 15506 10544 9819 ...
##  $ TotalDistance           : num  8.5 6.97 6.74 6.28 8.16 ...
##  $ TrackerDistance         : num  8.5 6.97 6.74 6.28 8.16 ...
##  $ LoggedActivitiesDistance: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveDistance      : num  1.88 1.57 2.44 2.14 2.71 ...
##  $ ModeratelyActiveDistance: num  0.55 0.69 0.4 1.26 0.41 ...
##  $ LightActiveDistance     : num  6.06 4.71 3.91 2.83 5.04 ...
##  $ SedentaryActiveDistance : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveMinutes       : int  25 21 30 29 36 38 42 50 28 19 ...
##  $ FairlyActiveMinutes     : int  13 19 11 34 10 20 16 31 12 8 ...
##  $ LightlyActiveMinutes    : int  328 217 181 209 221 164 233 264 205 211 ...
##  $ SedentaryMinutes        : int  728 776 1218 726 773 539 1149 775 818 838 ...
##  $ Calories                : int  1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...

head(sleep_day)

##           Id              SleepDay TotalSleepRecords TotalMinutesAsleep
## 1 1503960366 4/12/2016 12:00:00 AM                 1                327
## 2 1503960366 4/13/2016 12:00:00 AM                 2                384
## 3 1503960366 4/15/2016 12:00:00 AM                 1                412
## 4 1503960366 4/16/2016 12:00:00 AM                 2                340
## 5 1503960366 4/17/2016 12:00:00 AM                 1                700
## 6 1503960366 4/19/2016 12:00:00 AM                 1                304
##   TotalTimeInBed
## 1            346
## 2            407
## 3            442
## 4            367
## 5            712
## 6            320

colnames(sleep_day)

## [1] "Id"                 "SleepDay"           "TotalSleepRecords" 
## [4] "TotalMinutesAsleep" "TotalTimeInBed"

str(sleep_day)

## 'data.frame':    413 obs. of  5 variables:
##  $ Id                : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ SleepDay          : chr  "4/12/2016 12:00:00 AM" "4/13/2016 12:00:00 AM" "4/15/2016 12:00:00 AM" "4/16/2016 12:00:00 AM" ...
##  $ TotalSleepRecords : int  1 2 1 2 1 1 1 1 1 1 ...
##  $ TotalMinutesAsleep: int  327 384 412 340 700 304 360 325 361 430 ...
##  $ TotalTimeInBed    : int  346 407 442 367 712 320 377 364 384 449 ...

head(hourly_steps)

##           Id          ActivityHour StepTotal
## 1 1503960366 4/12/2016 12:00:00 AM       373
## 2 1503960366  4/12/2016 1:00:00 AM       160
## 3 1503960366  4/12/2016 2:00:00 AM       151
## 4 1503960366  4/12/2016 3:00:00 AM         0
## 5 1503960366  4/12/2016 4:00:00 AM         0
## 6 1503960366  4/12/2016 5:00:00 AM         0

colnames(hourly_steps)

## [1] "Id"           "ActivityHour" "StepTotal"

str(hourly_steps)

## 'data.frame':    22099 obs. of  3 variables:
##  $ Id          : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ ActivityHour: chr  "4/12/2016 12:00:00 AM" "4/12/2016 1:00:00 AM" "4/12/2016 2:00:00 AM" "4/12/2016 3:00:00 AM" ...
##  $ StepTotal   : int  373 160 151 0 0 0 0 0 250 1864 ...

Data Cleaning

## Understanding Summary Statistics
# How many unique participants are there in each data set
n_distinct(daily_activity$Id)

## [1] 33

n_distinct(sleep_day$Id)

## [1] 24

n_distinct(hourly_steps$Id)

## [1] 33

# Checking for duplicates
sum(duplicated(daily_activity))

## [1] 0

sum(duplicated(sleep_day))

## [1] 3

sum(duplicated(hourly_steps))

## [1] 0

# Removing Duplicates
daily_activity <- daily_activity %>%
  distinct() %>%
  drop_na()

sleep_day <- sleep_day %>%
  distinct() %>%
  drop_na()

hourly_steps <- hourly_steps %>%
  distinct() %>%
  drop_na()

# Verifying remove duplicates
sum(duplicated(sleep_day))

## [1] 0

daily_activity <- daily_activity %>%
  rename(date = ActivityDate) %>% 
  mutate(date = as.Date(date, format = "%m/%d/%Y"))

sleep_day <- sleep_day %>% 
  rename(date = SleepDay) %>% 
  mutate(date=as.Date(date, format = "%m/%d/%Y %I:%M:%S %p" , tz = Sys.timezone()))


str(daily_activity)

## 'data.frame':    940 obs. of  15 variables:
##  $ Id                      : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ date                    : Date, format: "2016-04-12" "2016-04-13" ...
##  $ TotalSteps              : int  13162 10735 10460 9762 12669 9705 13019 15506 10544 9819 ...
##  $ TotalDistance           : num  8.5 6.97 6.74 6.28 8.16 ...
##  $ TrackerDistance         : num  8.5 6.97 6.74 6.28 8.16 ...
##  $ LoggedActivitiesDistance: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveDistance      : num  1.88 1.57 2.44 2.14 2.71 ...
##  $ ModeratelyActiveDistance: num  0.55 0.69 0.4 1.26 0.41 ...
##  $ LightActiveDistance     : num  6.06 4.71 3.91 2.83 5.04 ...
##  $ SedentaryActiveDistance : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ VeryActiveMinutes       : int  25 21 30 29 36 38 42 50 28 19 ...
##  $ FairlyActiveMinutes     : int  13 19 11 34 10 20 16 31 12 8 ...
##  $ LightlyActiveMinutes    : int  328 217 181 209 221 164 233 264 205 211 ...
##  $ SedentaryMinutes        : int  728 776 1218 726 773 539 1149 775 818 838 ...
##  $ Calories                : int  1985 1797 1776 1745 1863 1728 1921 2035 1786 1775 ...

str(sleep_day)

## 'data.frame':    410 obs. of  5 variables:
##  $ Id                : num  1.5e+09 1.5e+09 1.5e+09 1.5e+09 1.5e+09 ...
##  $ date              : Date, format: "2016-04-12" "2016-04-13" ...
##  $ TotalSleepRecords : int  1 2 1 2 1 1 1 1 1 1 ...
##  $ TotalMinutesAsleep: int  327 384 412 340 700 304 360 325 361 430 ...
##  $ TotalTimeInBed    : int  346 407 442 367 712 320 377 364 384 449 ...

# convert date string to date-time in the hourly_steps.
hourly_steps<- hourly_steps %>%
  rename(date_time = ActivityHour) %>% 
  mutate(date_time = as.POSIXct(date_time,format ="%m/%d/%Y %I:%M:%S %p" , tz=Sys.timezone()))

head(hourly_steps)

##           Id           date_time StepTotal
## 1 1503960366 2016-04-12 00:00:00       373
## 2 1503960366 2016-04-12 01:00:00       160
## 3 1503960366 2016-04-12 02:00:00       151
## 4 1503960366 2016-04-12 03:00:00         0
## 5 1503960366 2016-04-12 04:00:00         0
## 6 1503960366 2016-04-12 05:00:00         0

Analyse Phase

Goal: To analyze FitBit User Trends and determine if it can help create a marketing strategy for BellaBeat

Perform Calcualtion

pulling statistics for analysis

count — no. of rows mean (average) std (standard deviation) min and max percentiles 25%, 50%, 75%

daily_activity

daily_activity %>%
  select(TotalSteps,
          TotalDistance,
          SedentaryMinutes,
          Calories) %>%
  summary()

##    TotalSteps    TotalDistance    SedentaryMinutes    Calories   
##  Min.   :    0   Min.   : 0.000   Min.   :   0.0   Min.   :   0  
##  1st Qu.: 3790   1st Qu.: 2.620   1st Qu.: 729.8   1st Qu.:1828  
##  Median : 7406   Median : 5.245   Median :1057.5   Median :2134  
##  Mean   : 7638   Mean   : 5.490   Mean   : 991.2   Mean   :2304  
##  3rd Qu.:10727   3rd Qu.: 7.713   3rd Qu.:1229.5   3rd Qu.:2793  
##  Max.   :36019   Max.   :28.030   Max.   :1440.0   Max.   :4900

#number of active minutes per category
daily_activity %>% 
  select(VeryActiveMinutes,
         FairlyActiveMinutes,
         LightlyActiveMinutes,
         SedentaryMinutes) %>% 
  summary()

##  VeryActiveMinutes FairlyActiveMinutes LightlyActiveMinutes SedentaryMinutes
##  Min.   :  0.00    Min.   :  0.00      Min.   :  0.0        Min.   :   0.0  
##  1st Qu.:  0.00    1st Qu.:  0.00      1st Qu.:127.0        1st Qu.: 729.8  
##  Median :  4.00    Median :  6.00      Median :199.0        Median :1057.5  
##  Mean   : 21.16    Mean   : 13.56      Mean   :192.8        Mean   : 991.2  
##  3rd Qu.: 32.00    3rd Qu.: 19.00      3rd Qu.:264.0        3rd Qu.:1229.5  
##  Max.   :210.00    Max.   :143.00      Max.   :518.0        Max.   :1440.0

# sleep
sleep_day %>% 
  select(TotalSleepRecords,
         TotalMinutesAsleep,
         TotalTimeInBed) %>%
  summary()

##  TotalSleepRecords TotalMinutesAsleep TotalTimeInBed 
##  Min.   :1.00      Min.   : 58.0      Min.   : 61.0  
##  1st Qu.:1.00      1st Qu.:361.0      1st Qu.:403.8  
##  Median :1.00      Median :432.5      Median :463.0  
##  Mean   :1.12      Mean   :419.2      Mean   :458.5  
##  3rd Qu.:1.00      3rd Qu.:490.0      3rd Qu.:526.0  
##  Max.   :3.00      Max.   :796.0      Max.   :961.0

# hourly
hourly_steps %>% 
  select(StepTotal) %>% 
  summary()

##    StepTotal      
##  Min.   :    0.0  
##  1st Qu.:    0.0  
##  Median :   40.0  
##  Mean   :  320.2  
##  3rd Qu.:  357.0  
##  Max.   :10554.0

Data Merging

combined_data <- merge(daily_activity, sleep_day, by=c ("Id", "date")) %>% 
  mutate(weekday = weekdays(as.Date(date,"m/%d/%Y")))

n_distinct(combined_data$Id)

## [1] 24

glimpse(combined_data)

## Rows: 410
## Columns: 19
## $ Id                       <dbl> 1503960366, 1503960366, 1503960366, 150396036…
## $ date                     <date> 2016-04-12, 2016-04-13, 2016-04-15, 2016-04-…
## $ TotalSteps               <int> 13162, 10735, 9762, 12669, 9705, 15506, 10544…
## $ TotalDistance            <dbl> 8.50, 6.97, 6.28, 8.16, 6.48, 9.88, 6.68, 6.3…
## $ TrackerDistance          <dbl> 8.50, 6.97, 6.28, 8.16, 6.48, 9.88, 6.68, 6.3…
## $ LoggedActivitiesDistance <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveDistance       <dbl> 1.88, 1.57, 2.14, 2.71, 3.19, 3.53, 1.96, 1.3…
## $ ModeratelyActiveDistance <dbl> 0.55, 0.69, 1.26, 0.41, 0.78, 1.32, 0.48, 0.3…
## $ LightActiveDistance      <dbl> 6.06, 4.71, 2.83, 5.04, 2.51, 5.03, 4.24, 4.6…
## $ SedentaryActiveDistance  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VeryActiveMinutes        <int> 25, 21, 29, 36, 38, 50, 28, 19, 41, 39, 73, 3…
## $ FairlyActiveMinutes      <int> 13, 19, 34, 10, 20, 31, 12, 8, 21, 5, 14, 23,…
## $ LightlyActiveMinutes     <int> 328, 217, 209, 221, 164, 264, 205, 211, 262, …
## $ SedentaryMinutes         <int> 728, 776, 726, 773, 539, 775, 818, 838, 732, …
## $ Calories                 <int> 1985, 1797, 1745, 1863, 1728, 2035, 1786, 177…
## $ TotalSleepRecords        <int> 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ TotalMinutesAsleep       <int> 327, 384, 412, 340, 700, 304, 360, 325, 361, …
## $ TotalTimeInBed           <int> 346, 407, 442, 367, 712, 320, 377, 364, 384, …
## $ weekday                  <chr> "Tuesday", "Wednesday", "Friday", "Saturday",…

How Active are the Users?

#Active users
active_users <- daily_activity %>%
  filter(FairlyActiveMinutes >= 21.4 | VeryActiveMinutes>=10.7) %>% 
  group_by(Id) %>% 
  count(Id) 

total_minutes <- sum(daily_activity$SedentaryMinutes, daily_activity$VeryActiveMinutes, daily_activity$FairlyActiveMinutes, daily_activity$LightlyActiveMinutes)
sedentary_percentage <- sum(daily_activity$SedentaryMinutes)/total_minutes*100
lightly_percentage <- sum(daily_activity$LightlyActiveMinutes)/total_minutes*100
fairly_percentage <- sum(daily_activity$FairlyActiveMinutes)/total_minutes*100
active_percentage <- sum(daily_activity$VeryActiveMinutes)/total_minutes*100

#Pie charts
percentage <- data.frame(
  level=c("Sedentary", "Lightly Active", "Fairly Active", "Very Active"),
  minutes=c(sedentary_percentage,lightly_percentage,fairly_percentage,active_percentage)
)


plot_ly(percentage, labels = ~level, values = ~minutes, type = 'pie',textposition = 'outside',textinfo = 'label+percent') %>% 
layout(title = 'Activity Level Minutes',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

How Much Steps Taken Per day

day_steps_sleep <- combined_data %>%
  mutate(weekday = weekdays(date))

day_steps_sleep$weekday <- ordered(day_steps_sleep$weekday, levels=c("Monday", "Tuesday", "Wednesday", "Thursday",
"Friday", "Saturday", "Sunday"))

 day_steps_sleep <- day_steps_sleep %>%
  group_by(weekday) %>%
  summarize (daily_steps = mean(TotalSteps), daily_sleep = mean(TotalMinutesAsleep))

head(day_steps_sleep)

## # A tibble: 6 × 3
##   weekday   daily_steps daily_sleep
##   <ord>           <dbl>       <dbl>
## 1 Monday          9273.        420.
## 2 Tuesday         9183.        405.
## 3 Wednesday       8023.        435.
## 4 Thursday        8184.        401.
## 5 Friday          7901.        405.
## 6 Saturday        9871.        419.

ggplot(day_steps_sleep) +
      geom_col(aes(weekday, daily_steps), fill = "#006699") +
      geom_hline(yintercept = 7500) +
      labs(title = "Daily steps per weekday", x= "Weekday", y = "Daily Steps") +
      theme(axis.text.x = element_text(angle = 45,vjust = 0.5, hjust = 1))

This shows that participants are most active on Saturdays and least active on Sundays.

How much Sleep Time Per Day

ggplot(day_steps_sleep, aes(weekday, daily_sleep)) +
      geom_col(fill = "#85e0e0") +
      geom_hline(yintercept = 480) +
      labs(title = "Minutes asleep per weekday", x= "Weekday", y = "Sleep") +
      theme(axis.text.x = element_text(angle = 45,vjust = 0.5, hjust = 1))

People Sleep more on Wednesdays and Sundays than any other days of the week. However this disparity is not that much

Tracker Usage across the week

 ggplot(data = combined_data, aes(x=weekday)) +
  geom_bar(fill="green") +
  labs(title = "Tracker usage across the week", x="Weekday")

More tracking is done on Thursdays, Tuesdays and Wednesdays.

ggplot(data=combined_data, aes(x=weekday, y=LoggedActivitiesDistance)) +
  geom_bar(stat="identity", fill="green")+
  labs(title="Logged Activity Distance by Day", y="Logged Activity Distance")

There are many blanks in the data and no records were available for Saturday and Sunday. The highest days of logged distance was on Monday.

Hourly Steps throughout the day

hourly_steps <- hourly_steps %>% 
  separate(date_time, into = c ("date", "time"), sep= " ") %>%
  mutate(date_time = ymd(date))

head(hourly_steps)

##           Id       date     time StepTotal  date_time
## 1 1503960366 2016-04-12 00:00:00       373 2016-04-12
## 2 1503960366 2016-04-12 01:00:00       160 2016-04-12
## 3 1503960366 2016-04-12 02:00:00       151 2016-04-12
## 4 1503960366 2016-04-12 03:00:00         0 2016-04-12
## 5 1503960366 2016-04-12 04:00:00         0 2016-04-12
## 6 1503960366 2016-04-12 05:00:00         0 2016-04-12

hourly_steps %>%
  group_by(time) %>%
  summarize(average_steps = mean(StepTotal)) %>%
  ggplot() +
  geom_col(mapping = aes(x=time, y = average_steps, fill = average_steps)) + 
  labs(title = "Hourly steps throughout the day", x="", y="") + 
  scale_fill_gradient(low = "green", high = "red")+
  theme(axis.text.x = element_text(angle = 90))

1. The Hourly step distribution shows that people are most active between 5pm and 10pm, this could be due to walking after work hours.

2. They are also highly active from 12pm to 2pm and this could be due to break from work.

3. Bellabeat can create a reminder feature in their app at free times (lunch break and close of work) to remind the users to increase their movement.

Correlation Between

Daily steps and calories

ggplot(data=combined_data, aes(x=TotalSteps, y=Calories)) + 
  geom_jitter() + geom_point() + 
  geom_smooth(color = "red") +
  labs(title = "Total Daily steps vs Calories", x = "Daily steps", y= "Calories")

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

This shows that there is a positive correlation between Total Steps and Calories. The more steps taken, the more calories burnt.

Daily steps and daily sleep

ggplot(data=combined_data, aes(x=TotalSteps, y=TotalMinutesAsleep)) + 
  geom_jitter() + geom_point() + 
  geom_smooth(color = "red") + 
  labs(title = "Daily steps vs Minutes Asleep", x = "Daily steps", y= "Minutes Asleep")

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

There is no correlation between daily activity level based on steps and the amount of minutes users sleep a day.

Sleep Time Vs Sendentary Time

ggplot(data=combined_data, aes(x=TotalMinutesAsleep, y=SedentaryMinutes)) + 
  geom_jitter() + geom_point(color='darkblue') + 
  geom_smooth(color = "red") + 
  labs(title = "Sleep Time vs Sedentary Minutes" )

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

1. There is a negative correlation between sedentary minutes and sleep time.

2. To increase the correlation, the Bellabeat app could recommend to its users to reduce sedentary time so that they can sleep better

Conclusion and Recommendations

After going through the 7 Analysis steps and despite the limitations of the Dataset (The data is from Fitbit and not bellabeat and the sample size is small and for a limited number of time), we can recommend the following for Bellabeat marketing strategy:

More accurate data from Bellabeat users should be collected so that a more accurate analysis could be generated.
To reduce the sedentary lifestyle of 81.3% of the users, and get more of them fairly or lightly active, a notification feature at specific timing to remind users could be added to the bellabeat app.
Bellabeat can create group activities or content that encourages people to be more active on low step and high sedentary day like Sundays.
Provide app notification for users to remind them to get sufficient sleep every day and implement new sleep measurement features or products such as tracking sleep time and Rapid Eye Movement (REM) sleep.
Bellabeat can consider setting daily/weekly calorie challenges and award points to users based on the top performers. Where the points can be accumulated and redeemed as a discount for their next product purchase.
Bellabeat can suggest some ideas for low calorie breakfast, lunch, and dinner foods to help users that want to lose weight.
Bellabeat marketing team can create campaigns, educational contents, exercise suggestions and trackers on the bellabeat app and host physical fitness events and activities.
The App should have a good user experience and should be seamless to use.

Bellabeat_Casestudy

Shukurat

2023-05-23

Process

Loading Packages

Load Dataset

Preview Loaded Dataset

Data Cleaning

Analyse Phase

Goal: To analyze FitBit User Trends and determine if it can help create a marketing strategy for BellaBeat

Perform Calcualtion

daily_activity

Data Merging

How Active are the Users?

How Much Steps Taken Per day

How much Sleep Time Per Day

Tracker Usage across the week

Hourly Steps throughout the day

Correlation Between

Daily steps and calories

Daily steps and daily sleep

Sleep Time Vs Sendentary Time

Conclusion and Recommendations

Bellabeat_Casestudy

Shukurat

2023-05-23

Process

Loading Packages

Load Dataset

Preview Loaded Dataset

Data Cleaning

Analyse Phase

Goal: To analyze FitBit User Trends and determine if it can help create a marketing strategy for BellaBeat

Perform Calcualtion

daily_activity

Data Merging

Share - Visualization

How Active are the Users?

How Much Steps Taken Per day

How much Sleep Time Per Day

Tracker Usage across the week

Hourly Steps throughout the day

Correlation Between

Daily steps and calories

Daily steps and daily sleep

Sleep Time Vs Sendentary Time

Conclusion and Recommendations